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Updated: Jan 22, 2026
11:15
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
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Comprehensive elaboration of database resources utilized in next-generation sequencing-based tumor somatic mutation
Peng Gao1, Rui Zhang2, Jinming Li1
1National Center for Clinical Laboratories, Beijing Hospital, National Center of Gerontology, Beijing, People's Republic of China; Graduate School, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People's Republic of China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, People's Republic of China.
Biochimica Et Biophysica Acta. Reviews on Cancer
|July 3, 2019
Summary
Understanding next-generation sequencing (NGS) databases is crucial for precision oncology. This review details NGS database functions, limitations, and solutions for accurate tumor genomic profiling in clinical settings.
Area of Science:
- Genomic Medicine
- Bioinformatics
Background:
- Precision oncology relies on next-generation sequencing (NGS) for tumor genomic profiling.
- Numerous databases support NGS analysis, but challenges remain in their application.
Purpose of the Study:
- To review the role and applications of databases in NGS-based somatic mutation detection.
- To compare different databases used in sequence alignment, variant filtration, and interpretation.
- To identify database limitations and propose complementary solutions for clinical utility.
Main Methods:
- Literature review of typically used databases for NGS analysis.
- Comparative analysis of databases for sequence alignment, variant filtration, and interpretation.
- Identification of limitations and development of solutions for database integration.
Main Results:
- Databases are essential for various NGS analysis steps, including alignment, filtration, and interpretation.
- Significant differences exist between databases with similar functions, impacting clinical evidence.
- Limitations in current databases necessitate complementary strategies for robust NGS testing.
Conclusions:
- A comprehensive understanding of NGS databases is vital for effective precision oncology.
- Complementary use of databases and addressing their limitations enhance the reliability of NGS-based somatic mutation detection.
- An overview diagram illustrates database integration in the NGS pipeline for clinical application.
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